Skip to contents


This technical appendix describes how cond_indirect() from the package manymome (Cheung & Cheung, 2023) works internally to extract the parameters and compute a conditional indirect effect.


Workflow of manymome::cond_indirect()


Workflow of manymome::cond_indirect_effects()


Main workflow

Workflow of manymome::indirect_i()

For Call get_prod(), see the workflow of Creating prods.

prods not supplied

Creating prods

Workflow of manymome::indirect_i(): Creating prods


Latent variables

If all variables along a path are latent variables, product term(s) must be identified by their names because raw scores are not available.

Default uses "_x_". For example, f1_x_f2 is the product term between f1 and f2.

Extracting Point Estimates and Variance-Covariance Matrix

When the point estimates or variance-covariance matrix of the point estimates are needed, they will be extracted internally using functions developed for the fit object, which can be a lavaan-class object, a list of the outputs from stats::lm(), or a lavaan.mi-class object generated by fitting a model to several datasets using multiple imputation.


Cheung, S. F., & Cheung, S.-H. (2023). manymome: An R package for computing the indirect effects, conditional effects, and conditional indirect effects, standardized or unstandardized, and their bootstrap confidence intervals, in many (though not all) models. Behavior Research Methods.